标题： 规范流模型 显示英文标题

标题： Gauge Flow Models

摘要： 本文介绍了规范流模型，这是一种新型的生成流模型。 这些模型在流的常微分方程（ODE）中引入了一个可学习的规范场。 提供了这些模型的全面数学框架，详细描述了它们的构建和特性。 使用流匹配在高斯混合模型上的实验表明，规范流模型相比具有可比或甚至更大规模的传统流模型，性能显著更好。 此外，未发表的研究表明，在更广泛的生成任务中存在性能提升的潜力。 显示英文摘要

摘要： This paper introduces Gauge Flow Models, a novel class of Generative Flow Models. These models incorporate a learnable Gauge Field within the Flow Ordinary Differential Equation (ODE). A comprehensive mathematical framework for these models, detailing their construction and properties, is provided. Experiments using Flow Matching on Gaussian Mixture Models demonstrate that Gauge Flow Models yields significantly better performance than traditional Flow Models of comparable or even larger size. Additionally, unpublished research indicates a potential for enhanced performance across a broader range of generative tasks.